Prompt Guiding Explained

Everything you need to know about prompt guiding

Chaiverse: Prompting & Parameters Tuning Guide 🚀

  • Prompt Engineering is an essential part of making high-quality models.

  • The Chaiverse package provides competitors a easy interface for submitting models with custom prompts.

  • Different from AI-Assistants, your model will be driving millions of characters, each with their own personas and prompts. This demo will explain what are the key variables and how they are used.

High-Level Overview of Chai Characters 😍

Definition

  • There are ~5 million characters that are user-generated on the Chai App

  • Your model will be the "engine" which drives the conversations between these characters and users

  • Each character is defined by the following three things:

    • Character Name (bot_name): name of the character

    • Character Persona (memory): character's personalities

    • Example Conversation (prompt): character creator's provided example conversation

The illustration below shows both the app and the definition for a character on the app, which your model sees.

Context Window & Persistence 🪟

Given a fixed context window (the default is 512 tokens):

  • We want the model to constantly remember the character's persona, hence it is always present within the context-window and it is called memory

  • Example conversation matters less as the conversation evolves. As a result, it gets truncated as the actual conversation grows

Truncation

Intuitively, if the memory (i.e. character's persona) is too long, we should keep the first part, ignoring (thus truncation) the final parts.

If the prompt (i.e. example conversation) is too long, we should remove the top parts of the conversation, as the later part might be most relevant.

Therefore:

  • If the memory is too long, it is truncated FROM THE RIGHT

  • Whereas prompt is truncated FROM THE LEFT

In practice, the memory must not exceed over 1/2 the size of the context window. Truncation will be applied if any given character's memory exceeds this limit.

Essentially this is what your model will see

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